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» Predicting relative performance of classifiers from samples
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NIPS
2007
13 years 9 months ago
Predictive Matrix-Variate t Models
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn ...
Shenghuo Zhu, Kai Yu, Yihong Gong
ICMCS
2005
IEEE
129views Multimedia» more  ICMCS 2005»
14 years 1 months ago
Feature Selection and Stacking for Robust Discrimination of Speech, Monophonic Singing, and Polyphonic Music
In this work we strive to find an optimal set of acoustic features for the discrimination of speech, monophonic singing, and polyphonic music to robustly segment acoustic media st...
Björn Schuller, Brüning J. B. Schmitt, D...
ISCA
1999
IEEE
90views Hardware» more  ISCA 1999»
13 years 12 months ago
Correlated Load-Address Predictors
As microprocessors become faster, the relative performance cost of memory accesses increases. Bigger and faster caches significantly reduce the absolute load-to-use time delay. Ho...
Michael Bekerman, Stéphan Jourdan, Ronny Ro...
ISBI
2004
IEEE
14 years 8 months ago
Detection of Bronchovascular pairs on HRCT Lung Images Through Relational Learning
The identification of bronchovascular pairs on High Resolution Computer Tomography (HRCT) images provides valuable diagnostic information in patients with suspected airway disease...
Mithun Nagendra Prasad, Arcot Sowmya
PRL
2006
129views more  PRL 2006»
13 years 7 months ago
Learning spatial relations in object recognition
This paper studies two types of spatial relationships that can be learned from training examples for object recognition. The first one employs deformable relationships between obj...
Thang V. Pham, Arnold W. M. Smeulders